Software Applications for the Integration of Plug-In Hybrid Electric Vehicles within the Smart Grid

Shruti Ramaswamy and Dr. Alireza Khaligh

Abstract

Smart Grid is a new solution to the aging power grid. It is a budding web of controls, automation and intelligent technologies that work together to make the grid greener, secure and reliable. Its innovative dual-direction communication between the grid and the user establishes a system of regular updates to a home that details energy consumption on a real time basis. With an increasing Plug-in Hybrid Electric Vehicle (PHEV) market, consumers have another avenue for involvement with the grid. PHEV charging is a heavy load added to a home. If this extra load is not managed properly, the effects of this pressure may prove detrimental for the consumer and may result in unwanted power outages that may affect an entire residential community. Demand Response programs can help efficiently reduce this added pressure and minimize energy costs. This research explores the idea of developing a software application to manage consumer loads dynamically. The program manages times during the day/night a PHEV can be charged such that the total house load is managed and its cost is minimized. In addition to returning specific times to charge the PHEV, the program also seeks to build a smart home that shifts loads dynamically to save the consumer energy and money. The program divides household loads into three categories - critical, deferrable and interruptible, and returns savings calculated using Real Time Pricing, Time of Use Pricing and Flat Rate Pricing options. The goal is to create a user-friendly environment for the consumer to manage energy consumption reliably, effectively and economically.

Introduction

Figure 1: Benefits of Energy Storage Systems [6]

As the world continues to catch up with
changing technology, it leans closer to
face its dire need for change in the current
power driven market. Rigorous policies
backed by environmental laws, increasing consumer concerns and demand have
forced utility companies to consider updating the current power grid system from
its root. Internally too, utilities are dealing
with increasing problems stemming from
aging equipment and limited communication between utilities and consumers.
A solution, the Smart Grid is built with a
vision to "connect everyone to abundant,
affordable, clean, efficient, and reliable
electric power anytime, anywhere" [1]. To
further this idea, US Department of Energy has created the Office of Electricity
Delivery and Energy Reliability to provide
stronger leadership and serve as the epicenter of policy and technology development activities in the department related
to the electric grid.

In order to meet growing demands
and actively encourage consumer participation, America's electric system needs
to be modernized and expanded by government and industry. Involvement will
be accomplished using the grid's two way
flow of electricity and information between consumers and utilities. Effective
communication will provide consumers
with up-to-date information about their
energy consumption and money spent
using varied price options made available
to them. This new autonomy to control
and change household load consumption
to meet need, unwraps an assortment of
Demand Response (DR) programs aimed
at giving consumers options to conserve
energy and shift loads that will offer them
utmost savings.

Table I
Illustrates the household load profile and an illustration of
how the various loads will be handled using DR.

Incentive programs and a plethora of
price based programs give consumers with
PHEVs more options to actively participate in programs such as load shifting that
will benefit them directly.

The Smart Grid has the infrastructure
to enable the efficient use of the new generation PHEVs. The electrification of the
vehicle fleet can radically change our dependence on oil. PHEVs rely on battery
as opposed to conventional vehicles that
consume fossil fuels. While PHEVs are
not pollution free, emissions from PHEVs
are far less than conventional cars. 1kWh
of energy used in a PHEV releases 0.69kg
of carbon dioxide [12] while 1 gallon of
gasoline emits 8.8kg of carbon dioxide
into the atmosphere [13].

This paper explores the idea of bringing to the consumer a program that will
control and give homeowners considerable control over their energy consumption. The purpose of the paper is to build
a structure for the basis of the software
program from root up. It begins with
a brief background on the Smart Grid
in "Understanding the Smart Grid." "A
Smarter Home" builds a platform for the
advantages of the Smart Grid to be used.
A smart solution to assist communication
for the consumer is proposed in "A Solution." "Preliminary Results" presents results of the basic algorithm employed to
utilize demand response. Lastly, "Conclusions" and "Future Work" conclude with
results and future work on the progress
and expected prospects for the software
application.

Understanding the Smart Grid

The vision is for the Smart Grid to
be a fully automated power delivery network that supervises and controls every
customer and module. Built on a basis of
two-way flow of electricity and information, the Smart Grid has an inbuilt system
of checks and balances between the consumer and utility companies and all components that duly fall in between the two.
Its distributed intelligence coupled with
Wi-Fi communications and automated
system allows real time pricing options
and faultless interfaces among all nodes of
the electric network.

Figure 2: Household Load over 24-hour period [10]

Figure 3: Scope of This Technology

Features of the Smart Grid:Grid Synergy: The Smart Grid is a design
built to manage change dynamically. Connection between consumer and utilities
will be maintained through secure links
at high speed. Consumers will receive real
time updates for price and energy and can
thus control their energy consumption
concurrently rather than having to wait
for monthly updates from power companies. Utilities are already investing greatly
in Smart Meters and Advanced Metering
Infrastructure (AMI) as the first step to
secure the prospect of two-way communication between the home and utility company [4].An Automated System: In addition to
contributing to reliable and secure elec-
tricity and information, Smart Grids open
up an array of possibilities for utilities
and consumers. Distributed Generation
(DG) at a residential level including mi-
cro turbines, solar photovoltaic cells, wind
turbines and grid energy storage enable
increased bi-directional power flow be-
tween power distributors and end-users.
A smarter grid will add resiliency to our
electric power system and make it better
prepared to address emergencies such as
severe storms, earthquakes, terrorist at-
tacks and blackouts. The interactive nature
of the Smart Grid will allow for automatic
rerouting of information when the equip-
ment fails. This will help minimize outages
when they do happen.

Communications Framework: Fiber op-
tics, microwave, infrared, power line carriers (PLC), wireless radio carriers such
as GSM and CDMA [4], transfer massive
amounts of data. Together they make up
the network most communication is built
on. Wireless communication will enable
connections between devices, homes and
utilities and information will be sent so all
data may be received and managed on a
real time basis. By establishing a constant
requirement for communication between
homes and utilities, security of information can be preserved and constantly improved.

Increased Grid Visibility: A key component of distribution intelligence is outage
detection and response. Today, outages are
detected based on customer phone calls
from an area. Superior automation technology with the help of smart meters will
enable grid operators to detect outages as
instantly as power is lost. Operators can
thus isolate a sector facing a power outage
and send technicians to immediately fix the
problem area. Another feature of this automation technology allows for newer and
well-developed visualization techniques
that interpret large amounts of data into
information that can be easily understood
by the consumer.

Figure 4: PHEV Charging Time Algorithm

Figure 5: Pictorial Representation of Coded Algorithm

Consumer Benefits from the Smart
Grid:
Smart Meters provide dynamic information that gives consumers real-time
updates on energy consumption and management. Dynamic monitoring of household data gives consumers instant reach to
information as opposed to having to wait
for monthly statements to determine usage patterns. Customers may now actively participate in three [5] ways. (1) First,
customers can reduce their consumption
of electricity at peak hours. By reducing
their electricity, the drop in demand may
be able to ease some pressure off the grid.
If this action results in a significant shift in
pressure at peak hours, grid operators will
notice lesser demand in power that will in
turn reduce over all price of power at a
peak hour. (2) Secondly, the customer may
be able to shift heavy power consuming
loads operating at peak hours to off-peak
hours. While the same amount of power is
demanded off the grid, the consumer may
be able to save money by operating his/
her device at hours when the system demand for power is low (3). Thirdly, a customer can alter cost significantly by onsite
generation of power. Installation of solar
panels and backyard wind turbines can
help a customer significantly. A consumer
may no longer need to alter his/her energy
consumption practices according to peak
hours. However, from a utilities perspective, electricity demand patterns will not
see significant changes unless an entire
residential community adopts onsite generation practices.

Understanding Energy Storage: Energy storage is defined as the conversion
of electrical energy from the power grid
into a form that can be stored until used
again when converted back into electrical
energy [6]. Research in different technologies has made available an array of storage
options. While energy storage is a heavily
researched subject, breakthroughs made in
the field have potential to heavily reduce
costs and maintain stability in the power
grid. Figure 1 describes the potential benefits of energy storage systems.

A Smarter Home

Building a Smart Home: This section focuses on building a home that has
various appliances that can be found in a
typical house. The appliances are divided
into three distinct load profiles: critical,
deferrable and interruptible. Loads that
fall under critical loads run irrespective of
time of day or peak hours. These are critical to a household and operate at all hours.
Loads that fall under deferrable loads run
at certain hours in continuity. They can
however, be shifted to off-peak hours to
reduce cost from functioning during peak
hours for the homeowner. Loads that fall
under interruptible loads can be run and
discontinued without consequence and
negligible discomfort to the homeowner.

Figure 6: Load Shifting - Demand Response Class Structure

Figure 7: Load Shifting - Demand Response Algorithm

One may note that the household demands peak load between 12pm- 3pm and
between 7pm-10pm. It must also be noted
that households pay more money during
peak hours than off-peak hours. This sort
of pricing option falls under Time of Use
(TOU) or Real Time Pricing (RTP).

To keep with varied pricing options,
the smart meters/advanced metering infrastructure (AMI) give consumers' constant updates on their load and price. The
consumer may shift loads accordingly to
off-peak hours to minimize costs on the
same. The manual process of monitoring household loads may pose a series
of inconveniences for the homeowner
as loads may need to be turned on and
off manually at all times. Load shifting
is an effective method under consumer
control. Figure 2 provides two distinct
graphs denoting household loads prior
to load shifting and an updated profile
of the household load with a PHEV that
does not increase with the addition of the
PHEV. On the contrary, energy is curtailed due to effective load shifting. Furthermore, incentive programs, offered
by utilities to actively manage load shifting give consumers enough reason to
seek an effective solution. Luckily, the
technology market provides a relatively easy solution. Increased knowledge
on technology and DR programs can
be used to build a system that can be
customized as per the requirements of
the homeowner. Figure 3 describes the
scope of such a system that is built to
benefit the consumer in numerous ways.

A Solution

The most unique quality of the Smart
Grid is its establishment of the two-way
information share of electricity and data.
This section highlights how such a connection can be used to ease into a homeowner's life and save him/her money by
executing simple shifts performed over a
24-hour period. While the future of the
program will be a software application
available for smart phones, it is essential
to understand the similar underlying algorithm that is be displayed over a different platform to perform the same function.

Table II

Table III

A JAVA Framework: Created as a
JAVA project, the framework consists of
two subdivisions of classes that act as separate entities.

Figure 4 deals with PHEV charging
under three pricing options- RTP, TOU
and Flat rate. Divided into two classes, the
PHEV Charge Controller class is the main
control unit within which various aspects
of the class and base class exist. The program is run with consumer input of his/
her choice of pricing option and desired
time to start charge. Another input that
would be required to calculate this information is the battery capacity that will be
extracted from the engine combustion
unit of the vehicle itself.

As the program is created using a Chevy
Volt battery design, the maximum usable
battery capacity is limited at 10.4kWh. It
is this 10.4kWh capacity that enables the
program to calculate the number of hours
left for the battery to attain full charge. The
instantaneous result is a price index for the
selected hours and a time index that is representative of the number of hours left for
the battery to attain full charge. While the
controller runs the main program, primary functions to calculate hours, time and
price index are handled by a helping class.
Figure 5 depicts the algorithm followed in
code to compute the best time to charge
the PHEV under given specifications.

Figure 8: ComEd Pricing Options and PHEV charge based on
scenario

Figure 6 integrates the basic algorithm,
as portrayed in Figure 4, into a larger program. The larger program, a super class,
deals with all of the basic appliances in a
household by segregating appliances into
three load profiles; critical, deferrable, and
interruptible. Like its first subdivision, this
part, too, is divided into classes. One class
holds the control that begins the program;
three other sub classes inherit methods that
are used to compute answers specific to
appliances concerned with each subclass.

The code uses maps (consisting of keys
with values) to store appliances and their
associated loads that are then transferred
into a larger map that holds the time of
day the appliance and its associated load at
that particular time. Here, time is used as
the common index of reference to attach
appliances with their individual associated
cost. These values are then arranged to determine cost of the household over a 24-hour period.

Figure 7 represents the algorithm of the
classes covered in code to calculate savings
when loads are shifted during times when prices
are high which in turn helps to manage loads.

Preliminary Results

The program uses maps to read from
three different pricing options namely,
RTP, TOU and Flat Rate. After relevant
data is accessed from the program, the
consumer is prompted to input a time he/
she would like to begin charging. Based on
the time, the program calculates the best
time index to charge the vehicle in order
to save the most money.

Table II [11] contains the assumptions
made for portrayal purposes with a time
and price index. The graph seen in Figure
8 is representative of the results the consumer will see with estimates on savings
on price using each pricing model, thereby
giving the consumer a better idea of how
much he/she can save if the PHEV is
charged at suggested times.

Figure 9: Household loads before any shift

Figure 10: Household Loads after Shifting Load

To demonstrate how the program will
be used, the program uses a few appliances
per load type. The functions are used on a
smaller scale so inconsistencies can be debugged and fixed instantly. Data is divided
into maps and stored as described in Table
III. The following graphs in Figure 9 and
Figure 10, give the reader a visual portrayal of how the code would internally shift
loads if the peak load under the base case
scenario (no PHEV) were exceeded.

The max peak is a line measured using
load calculations from appliances alone.
PHEV load is not measured in this account for peak load so as to show how
a smart home may manage its appliances
economically with little difference caused
by a new PHEV load.

Conclusion

This paper studies the integration of
technology into consumer life to aid and
control residential using advanced and superior technology enabled by Smart Grid
developments.

The Smart Grid is a budding web with
no beginning or end. Consumer homes,
generators and electrical appliances will be
connected without bias so energy flow is
adequately and securely maintained under
all conditions. It ensures a two-way flow
of electricity and information between the
power plant and the smart meter, which
will be installed in every home. The paper
helps further the use of the smart meter
by using its readings to create a scenario
of a typical household. This is created to
paint a realistic picture of load shifting
and PHEV charging with the primary aim
of minimizing cost for the consumer.

Using RTP and TOU rates from
ComEd for the Chicago area, the program
is built keeping the consumer's needs in
mind. The Java framework, using parameters for peak price and load, is able to shift
loads dynamically as per consumer convenience to determine significant savings per
household.

Future Work

Need for Disaster Management:
Today, interruption of electricity due to
blackouts can begin a series of botches
that can affect communications, signals,
security and traffic [14]. In places that
are too hot or cold or places that require
constant heat or cold suffer greatly and
in turn begin another domino effect of
failures including losses in infrastructure
and personal assets. A smarter grid with
automated self-healing features and adaptive technology will strengthen the power
grid making it more resistive to natural
and man-made attacks. Such a grid will
help minimize outages and minimize loses when hit.

The system is built so when an area
or sector loses power, it is isolated from
the rest of the grid so neighboring areas
may function unaffected while the isolated sector is located and power is restored
immediately. With the Smart Grid functioning as an interactive web of network
and information, each household is built
individually yet fully connected to other
houses in the community. This way when
one house loses power, it is isolated so
other houses remain unaffected.

Disaster Management is an important
concern that can be tackled using clean
and effective programming. Figure 8 depicts a possible algorithm that a program
may follow to handle stress and spontaneous failures on the grid. While all of the
code is written using JAVA, future work
would include working to build a smart
phone application so a consumer may be
able to control his/her energy consumption at the palm of his/her hand. The
technology market is changing quickly
and dramatically. Today, a majority of
homeowners own smart phones. Hence,
a free software application that enables a
homeowner to minimize his/her electricity bill and manage energy with his/her
smart phone should be made available as
soon as possible.

Figure 11: Possible Algorithm for Disaster Management

Exploring Storage Options: Significant research is being made on energy
storage integrated with the Smart Grid.
Research thus far has external storages
functioning as individual units to supply
power only when needed and recharged
at the earliest convenience. One such energy storage container is a disused PHEV
battery that can be installed in a consumer
home.

PHEV batteries that have end of life 80%
capacity supply power seamlessly and with
very little or no struggle. Benefits include:
energy arbitrage, ancillary services-regulation, spinning and non-spinning reserve
and back-up energy. Energy arbitrage refers to charging batteries at off-peak hours
and discharging power to appliances at
peak hours to utilize differences in energy
prices and minimize cost. Ancillary services are divided into various classes but the
most common types include regulation,
spinning and non-spinning reserves.

Regulation, the highest quality ancillary
services [8], is used to match the frequency and voltage of the grid by unerringly
matching dynamic energy demand and
supply.

Backup energy refers to using a reusable
PHEV battery to supply power in case of
an emergency resulting in a blackout. Although PHEV batteries may not be able
to supply power to the entire household,
the battery could provide enough power
to run critical loads such as heating in winter months and home security.

Ethical Considerations

As engineers, we are expected to perform to our highest potential because our
work and research may directly or indirectly
influence future research in the field. As students, it is harder to understand the direct
implications of our work in the real world.
We face a constant need to push ourselves
to perform to the best of our abilities and
work on retrieving data that is accurate and
meaningful.

The JAVA program discussed in this
paper is developed using an original algorithm. That being said, there is immense
room for improvement. Scenarios used to
drive a point, are based on educated guesses
of an average household that contains basic appliances one may not be able to do
without. Thus while the program itself is
not ready to be developed into a software
application for smart phones, the program
does provide a backbone to the algorithm
that will be used to accomplish this technological goal.

Acknowledgements

This work has been supported by the
U.S. National Science Foundation under Grant number 0852013, which is greatly acknowledged.

References

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